docstr: Get the text in the predicted images.
  Args:
      images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
      paths (list[str]): The paths of images. If paths not images
      output_dir (str): The directory to store output images.
      visualization (bool): Whether to save image or not.
  Returns:
      res (list): The result of text detection box and save path of images.
docstr_tok: ["Get","the","text","in","the","predicted","images",".","Args",":","images","(","list","(","numpy.ndarray",")",")",":","images","data",",","shape","of","each","is","[","H",",","W",",","C","]",".","If","images","not","paths","paths","(","list","[","str","]",")",":","The","paths","of","images",".","If","paths","not","images","output_dir","(","str",")",":","The","directory","to","store","output","images",".","visualization","(","bool",")",":","Whether","to","save","image","or","not",".","Returns",":","res","(","list",")",":","The","result","of","text","detection","box","and","save","path","of","images","."]
code: def recognize_text(
      self,
      images=[],
      paths=[],
      output_dir="ocr_result",
      visualization=False,
  ):
      """
      Get the text in the predicted images.
      Args:
          images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
          paths (list[str]): The paths of images. If paths not images
          output_dir (str): The directory to store output images.
          visualization (bool): Whether to save image or not.
      Returns:
          res (list): The result of text detection box and save path of images.
      """
      all_results = self.model.recognize_text(
          images=images,
          paths=paths,
          output_dir=output_dir,
          visualization=visualization,
      )
      return all_results
  
file_path: PaddleHub/modules/image/text_recognition/tamil_ocr_db_crnn_mobile/module.py
imports: import paddlehub
  from paddleocr.ppocr.utils.logging import get_logger
  from paddleocr.tools.infer.utility import base64_to_cv2
  from paddlehub.module.module import moduleinfo, runnable, serving
label_desc: Use image node_id 2 for calling the TamilOCRDBCRNNMobile obj's underlying member method code with example usage: obj.recognize_text(images, paths, output_dir, visualization) and returns: all_results